Abhineet Singh
Papers
6
Total Citations
37
H-Index
4
About
Abhineet Singh is a robotics and computer vision researcher whose work focuses on advancing visual tracking for high-precision robotic manipulation. His key research areas include real-time object tracking, perceptual grouping, and visual servoing for fine manipulation tasks. Singh’s major contributions center on developing robust, accurate tracking frameworks that bridge the gap between learning-based and registration-based paradigms. His most cited work, "Real-time salient closed boundary tracking via line segments perceptual grouping" (11 citations), introduces a novel method that integrates line detection with perceptual grouping to track object boundaries in real time—a critical capability for dynamic environments. He also created the Modular Tracking Framework (MTF), an open-source C++ library designed for robotics applications that enables high-precision, 4-DoF tracking essential for fine manipulation tasks. His paper "RKLT: 8 DOF Real-Time Robust Video Tracking" (8 citations) addresses the trade-off between tracker robustness and accuracy by combining coarse RANSAC features with accurate template registration. Singh’s work has been cited over 37 times, demonstrating its impact on the field of visual tracking and robotic control.
Research Focus
Key Achievements
Top Papers
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- 34-DoF Tracking for Robot Fine Manipulation Tasks5 citations · 2017
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- 5Modular tracking framework: A fast library for high precision tracking4 citations · 2017
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